An Edge and Trustworthy AI UAV System With Self-Adaptivity and Hyperspectral Imaging for Air Quality Monitoring

Leveraging the mobility and flexibility of unmanned aerial vehicles (UAVs), the proposed edge and trustworthy artificial intelligence (AI) UAV system (ETAUS) offers a comprehensive approach to air quality monitoring, complementing fixed monitoring stations. We propose a new convolutional neural network (CNN) model that utilizes hyperspectral imaging (HSI) data as input, enabling ETAUS to directly and accurately classify air quality index (AQI) levels without relying on a back-end AI computing platform. Additionally, ETAUS employs an FPGA-based system architecture, allowing for the integration of a neural engine, cryptographic hardware modules, and hardware protection matrices into a single FPGA device for accelerated processing, edge AI, and trustworthy AI functionalities. Based on its self-adaptivity, edge AI models, cryptographic hardware modules, and protection matrices can also be dynamically loaded into the system to support diverse functional requirements. Experiments have demonstrated that using our proposed CNN model and HSI data, the accuracy of AQI level classification can be achieved to 86.38%. ETAUS can achieve a speedup of $2.28\times $ to $36.9\times $ in terms of frames per second (FPS) for AQI-level classification compared to microprocessor-based and embedded GPU-based designs. ETAUS can also enhance energy efficiency by $2.7\times $ compared to embedded GPU solutions, such as NVIDIA Jetson Nano. To support all the cryptographic functions and protection matrices, system adaptivity in ETAUS can significantly increase resource utilization while decreasing power consumption by up to 2.79%.